code: def process(self, document):
      dataset = Dataset(
          document,
          self.config,
          self.pretrain,
          vocab=self.vocab,
          evaluation=True,
          sort_during_eval=True,
      )
      batch = iter(
          dataset.to_loader(batch_size=self.config["batch_size"])
      )
      preds = []
  
      idx = []
      with torch.no_grad():
          if self._tqdm:
              for i, b in enumerate(tqdm(batch)):
                  idx.extend(b[-1])
                  preds += self.trainer.predict(b)
          else:
              for i, b in enumerate(batch):
                  idx.extend(b[-1])
                  preds += self.trainer.predict(b)
  
      preds = unsort(preds, idx)
      dataset.doc.set(
          [doc.UPOS, doc.XPOS, doc.FEATS], [y for x in preds for y in x]
      )
      return dataset.doc
  
file_path: stanza/stanza/pipeline/pos_processor.py
imports: import torch
  from stanza.models.common import doc
  from stanza.models.common.utils import unsort
  from stanza.models.common.vocab import VOCAB_PREFIX, CompositeVocab
  from stanza.models.pos.data import Dataset
  from stanza.models.pos.trainer import Trainer
  from stanza.pipeline._constants import *
  from stanza.pipeline.processor import UDProcessor, register_processor
  from stanza.utils.get_tqdm import get_tqdm
label_desc: Use image node_id 7 for calling the POSProcessor obj's underlying member method code with example usage: obj.process(document) and returns: dataset